Honey bees (Apis mellifera) modify plant-pollinator network structure, but do not alter wild species’ interactions

Honey bees (Apis mellifera) are widely used for honey production and crop pollination, raising concern for wild pollinators, as honey bees may compete with wild pollinators for floral resources. The first sign of competition, before changes appear in wild pollinator abundance or diversity, may be changes to wild pollinator interactions with plants. Such changes for a community can be measured by looking at changes to metrics of resource use overlap in plant-pollinator interaction networks. Studies of honey bee effects on plant-pollinator networks have usually not distinguished whether honey bees alter wild pollinator interactions, or if they merely alter total network structure by adding their own interactions. To test this question, we experimentally introduced honey bees to a Canadian grassland and measured plant-pollinator interactions at varying distances from the introduced hives. We found that honey bees increased the network metrics of pollinator and plant functional complementarity and decreased interaction evenness. However, in networks constructed from just wild pollinator interactions, honey bee abundance did not affect any of the metrics calculated. Thus, all network structural changes to the full network (including honey bee interactions) were due only to honey bee-plant interactions, and not to honey bees causing changes in wild pollinator-plant interactions. Given widespread and increasing use of honey bees, it is important to establish whether they affect wild pollinator communities. Our results suggest that honey bees did not alter wild pollinator foraging patterns in this system, even in a year that was drier than the 20-year average.

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Introduction 41 With the widespread use of managed honey bees (Apis mellifera) for crop pollination and 42 honey production in parts of the world where they are not native, many studies have suggested 43 that high honey bee densities negatively affect wild pollinators through interference or 44 exploitative competition (reviewed in (1,2). Honey bees visit a wide variety of flower species (3) 45 and high honey bee density has been linked to declines in wild pollinator diversity (4), 46 abundance (5), floral resource use (6), and fitness (7), all via competition (8). 47 However, the first sign of competition between honey bees and wild pollinators may be 48 changes to which flower species wild pollinators visit in the presence of honey bees, versus when 49 honey bees are not present (5,6). If a wild pollinator is displaced by honey bees from one or more 50 of its usual flower species (as reported for other competitive relationships between pollinators; 51 e.g., (9,10), and for honey bees, (7)), the total set of flowers that the wild pollinator visits will 52 either decrease (as in (11)), or will shift to include new flower species not previously visited by 53 that pollinator species (12). Additionally, if honey bee use of a flower depletes nectar or pollen 54 (13), the frequency of visits by wild pollinator species to that flower species may decrease, even 55 if visitation does not cease altogether (14  (Fig 1a), which comprise the set of interactions ("links") observed between species ("nodes").

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These networks represent the pattern of interactions between all species within the plant-67 pollinator community that are included in the network model (18 We hypothesized that honey bees would compete with wild pollinators (8), excluding 120 some wild pollinator species from some of their normal flower species (7), and thus narrowing 121 their floral niches (5). We therefore predicted that in networks restricted to wild plant-pollinator 122 interactions, we would see decreases in generality and pollinator niche overlap, and an increase 123 in pollinator functional complementarity, with increasing honey bee abundance (Fig 1b). We also 124 hypothesized that honey bees would dominate interactions with their preferred plant species (6), 125 causing these plant species to be visited by fewer pollinator species (22). We thus predicted that 126 in networks of just wild plant-pollinator interactions, we would see decreases in vulnerability and 127 plant niche overlap and an increase in plant functional complementarity, with increasing honey 128 bee abundance (Fig 1b). 129 We also calculated six other commonly reported network metrics to maximize  We expected to see the strongest effects of honey bees on native plant-pollinator 139 interactions in midsummer (Fig 1a.ii) and therefore analyzed a midsummer dataset separately 140 from the full season dataset. Honey bee colonies comprise between 10,000 to 50,000 individuals, 141 attaining maximum size (34) and foraging distance (35) in midsummer. 142 We also examined how honey bee abundance affected network metrics for a bees-only 143 version of the full season network (Fig 1a.

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Experimental design 162 We arranged three clusters of honey bee hives, set at least 3 km apart, on the Mattheis

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Research Ranch, which is in mixedgrass prairie rangeland near the town of Brooks, Alberta,  Table S1). Livestock have grazed these rangelands for a    (Table S1).   Table S2.

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Occasionally, when flowers were not present on the marked transect, we moved the transect ≤ 10 203 m from its original location to reach flowers near the original transect demarcation. Moving the 204 transect did not change the distance to the hives.

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After July 9, 2019, sampling at one 5000 m transect "F5000" was halted after many 206 honey bees were observed (presumably from a feral or unregistered hive, see Fig S1), since this 207 replicate was intended to be a low-honey bee density transect. A new 5000 m transect, "G5000",

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was selected approximately 8000 m away from the northern 48-hive cluster (Fig 2). We treated 209 these transects as distinct site replicates but controlled statistically for the lower number of  Table S1).

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Network metric calculation 214 We pooled the observed insect-flower visitor interactions across the season for each 215 transect, to construct one bipartite network for each transect, for a total of 19 networks (6 x 3 216 distances from hives, plus the new 5000 m transect "G5000" that was established mid-season).

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We calculated the six network metrics described above to analyze honey bee effects on We used linear regression to test the effect of honey bee abundance on each of the six 236 resource-overlap-related and the six stability-related network metrics. We assessed Pearson 237 correlation coefficients between these metrics and found pollinator and plant functional 238 complementarity were highly correlated, as well as link density and vulnerability (Table S3). We 239 kept all metrics in the analysis, because these were ecological results rather than consequences of 240 the metric definitions and might differ for different datasets. We pooled honey bee abundance 241 (total number of honey bees observed visiting flowers) across the full season for each transect, 242 and divided by number of collection rounds at that transect, to calculate "honey bee abundance" 243 at a transect ( Fig S1), which we used as the predictor variable in all full season statistical models. 244 We used honey bee abundance rather than treatment (distance from honey bee hives) as the 245 predictor variable because distance from hives was not as closely associated with honey bee 246 abundance as might have been expected (Fig S1), so honey bee abundance at a transect seemed 247 like a better representation of honey bee "effect" at that transect, as in (23,25). 248 We tested the effect of honey bee abundance on each network metric for several datasets: visualize whether similar residuals were close together in space, and to examine whether a 260 special correlation structure was necessary to account for any spatial autocorrelation ( Fig S2). 261 We then ran generalized least squares (GLS) models with different correlation structures 262 (no correlation, corEcp, corGaus, corSpher, corLin, corRatio) for each response variable (nlme 263 package, (45)). After all models with correlation structures (including no special correlation 264 structure) were run, the model with the lowest AICc value was selected (MuMin package, (46)).

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The best models for each predictor variable did not include special correlation structures, so 266 simple linear regression models (SLR) were run. The exception was for modularity in the 267 13 without-honey bees network, so for this metric in this dataset, we ran a GLS model with the 268 corRatio correlation structure. 269 We then ran an additional multiple regression model (MR) for each response variable in 270 each dataset for which honey bee abundance was significant in the SLR. This was because 271 flower abundance, number of available flower species, and collection effort were positively 272 correlated with honey bee abundance (Table S4), so we included them as additional predictor  To test whether the effects of honey bee abundance on the network metrics differed mid-298 season, we created a mid-season all taxa dataset (Fig 1a.ii). To determine which sampling dates 299 the mid-season dataset should include, we divided the total honey bee abundance across the 300 season, from all transects pooled, into three roughly equal-length periods: "early", "mid" and 301 "late", based on natural breaks in the abundances of honey bees ( Fig S3). We then tested the 302 effects of honey bee abundance on all the same response variables as above, using the procedure 303 outlined above, for this mid-season all taxa dataset. Because some transects' networks were too 304 species-poor in the mid-season to obtain accurate values (A100, B100, B500, E100, F5000), they 305 were removed from this analysis.  49)). This calculated the proportion of the estimated total number of 321 unique interactions that our sampling detected (as in (36)). We repeated this at the transect level    (Table 2d) that were somewhat correlated with honey bee abundance (Table S4).

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In the second set of MR models (including all predictor variables), flower species 387 richness and total number of collections best explained most response variables (Table 2a).

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However, increasing honey bee abundance was still associated with significant increases in

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The partial regression coefficients are the expected change in the response variable associated 410 with a one unit change in a predictor variable holding the other predictor variables constant.

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Transformations are listed with the response variable.

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Effect of honey bees on the without-honey bees network structure 415 In contrast to all of the above results, the full season without-honey bees dataset with 416 only honey bee abundance as a predictor variable (SLR models) showed that honey bees did not 417 significantly affect pollinator functional complementarity, plant complementarity, or interaction 418 evenness, or any other network metric (Fig 3b, Table 2d). Although some P-values were low in 419 the SLR models (Table 1d), in the MR models, honey bee abundance was removed during model 420 selection in every case (Table 2d), suggesting that the low P-values in the SLR models could be 421 explained by correlated effects of the flower variables or collection effort better than they could  Table S8). when honey bee abundances were highest (compare regression coefficients in Table 2a to partial 436 regression coefficients in Table 2b). The exception was that pollinator functional 437 complementarity did not change significantly in the bees-only dataset (Table 2c). When looking 438 at the meta-networks (Fig S5), which had interactions pooled across transects by distance from 439 hives, similar to (26), rather than pooled by transect, the results were qualitatively similar (   The most unexpected result from our study was that we found no effect of honey bees on 480 nestedness, whether honey bee interactions were included or not ( The plant-pollinator network in this system appears to accommodate honey bee 503 interactions, integrating them into the system without major changes, despite honey bees taking a 504 central role (Fig S5). Honey bees may spread pathogens and parasites to some wild bee species 505 via shared flowers (53), or cause other changes to wild plant or pollinator fitness undetectable 506 from looking at changes to flower visitation (16). Furthermore, we did not detect all occurring 507 plant-pollinator interactions at our transects. Our estimated sampling completeness was 37% 508 overall (see Table S4), suggesting that we missed many rare interactions. Rare interactions 509 typically involve rare species, which may be particularly sensitive to competition from honey 510 bees (3). However, understanding honey bee effects on the subset of detected interactions is 511 useful because the most detectable interactions are the most frequent, and are those that 512 contribute most to ecosystem function (54).

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Additionally, our study was conducted in only one season, and plant-pollinator 514 interactions are known to be highly variable year to year (55). This variability is largely because 515 of species and interaction turnover, however, and network structure is expected to be more 516 consistent between years (55), so our conclusions about network structure might be expected to 517 hold in other years. The sampled year 2019 was drier than the 20-year average (Table S9),  (Table S10), and though the 521 data were not of high enough quality to publish, they indicated the same results.                 Table S7 for the full 1329 names of plant species) and the upper row depicts the pollinator species, coloured by their given 1330 taxonomic "group" or Order. For the full pollinator species list, see Table S6. "Moths" are